The Prediction of Time Trending Techniques in Manufacuturing

INTERNATIONAL JOURNAL OF INDUSTRIAL MANAGEMENT (IJIM)
ISSN: 2289-9286 e-ISSN: 0127-564X
VOL. 14, ISSUE 1, 580 – 587
DOI: https://doi.org/10.15282/ijim.14.1.2022.7547
*CORRESPONDING AUTHOR | Chun Chet Gan |  ccgan2906@gmail.com 580
© The Authors 2022. Published by Penerbit UMP. This is an open access article under the CC BY license.
ORIGINAL ARTICLE
THE PREDICTION OF TIME TRENDING TECHNIQUES IN MANUFACTURING
Ir. Gan Chun Chet
University of Manchester Institute of Science and Technology, United Kingdom.
P.Eng (Registered Engineer with Board of Engineers Malaysia).
ARTICLE HISTORY
Received: 22-3-2022
Revised: 13-4-2022
Accepted: 20-7-2022
KEYWORDS
Machine
Reduction
Setup
Time
Trending
INTRODUCTION
The search to find a suitable predictive equation, which can and could have helped in understanding the interpretation
of data, requires research in various areas before it can be applied. An idea, thought and agreed upon by a group of
researchers, remains th0061t it is falsifiable, which, if this happens, will mean that the data generated and the results
published are in question. In making belief ideas that fail when it comes to actual implementation, a set of ideas is
disproved to remove wrong thoughts that readers believe in. This would mean that the data collected or data analysis
leading to a conclusion was wrong.
The machine, of Italian make, has gears, shafts, and a timing flange with oil as its lubricator and its cooling system.
It runs on a single motor and multiple gear parts, timing shaft, vacuum erection, etc. Some of these are the same in the
latest machine design. It is highly thought off with many considerations which look like the first generation machine, the
use of limit switches instead of electronic cam timer. The machine is rigid and firm, with cast housing, polished shaft,
etc. The gears are designed with proper thoughts of an absolute long life span. When machine setups are required to be
performed, a team of skilled workers, with skills learned throughout its operation, performs the tasks with absolute
certainty as the work is done completely - from initial start to the end; fit and run.
The work was done entirely well throughout the years of training and coaching by seniors. Although with hiccups and
improper job tasks, the smooth workflow is fully paid-off with the co-operations of observant operators. The fitters and
operators' work produced the results management was hoping for years of patience. Is that the record time? The paper
writes with factual data on-time records achieved.
METHOD
In predicting a data set, the method utilised is critical to know whether it will accurately depict and consistently publish
to determine its reliability. This paper calculates the possible prediction estimates from the observable trend. Time
trending prediction technique based on moving averages or exponential smoothing, straightforward equations utilising
two past data (moving average) or utilising a past and a current prediction (the first prediction value based on experience
while the formula computes subsequent. See equations below) weighted (exponential smoothing), are utilised. This article
compares two mathematical equations with probability and categorisation methods to determine whether the results are
consistent. The curve shown here is close to a reducing exponential decay curve.
The two methods are simple to understand with the known errors to estimate a time. This paper discusses moving
average or exponential smoothing techniques to calculate a prediction value compared with probability and categorisation
ABSTRACT – The paper writes about utilizing time trend predictive techniques to calculate an
estimated prediction in machine setup application. The prediction is based on arithmetic equations
to estimate a future time. With these techniques in place, time trend predictors are able to know an
estimate time in setting up machine, which is a variable time factor, with known prediction limits.
Based on actual data collected over a period of time, a time trend is perceivable. It is concluded
that time trend techniques provide an estimate predictor, sufficient to know a rough value within a
categorical limit. In comparison with probability and categorization techniques, this paper shows
coherence findings. Machine setups are known of its variances due to human factors and other
uncontrollable factors. These activities, mainly repetitive in nature during parts change or
adjustment of parts, are done by trained personnel. The setups of the machine are difficult to
perform due to the complexity of the machine. The tasks are able to be performed by skilled
workers only. With the technical skills required to perform the tasks, it is unimaginable of the set
time to setup the machine and the target achieved by the job performers over the years. The
movements estimates, which is mentioned briefly in this paper, shows that it could estimate a time
to reduce the work hours. Besides time trend techniques, estimating the number of movements
can know the time to reduce the setup activities. The findings are reported as follows in this paper.

Gan │ International Journal of Industrial Management │ Vol. 14, Issue 1 (2022)
581 journal.ump.edu.my/ijim ◄
techniques. This paper also discusses the possibility of steep curves or step improvements from the observed time trend.
Two charts are developed to ease the understanding of this setup sub-routine.
Setup Time – The Actual Time Trend
What was unknown is that the time to set up the machine is taking too long, and there might be doubts about job
performers attending to other tasks while the production is waiting for job performers to attend to the activity. The priority
was set for the week and is critical to the team to meet the target to avoid a supply shortage. Production is nearly obsolete
yet still in use, and the technology involved remains today. The new generation of the machine turns out to be another
replicate of the traditionalist instead of a futurist view of change. The latest technology is included in the new packaging
machine.
The old machine is firm and rigid, and the technology still applies today, with improvements brought into the new
machine. Fig. 1 and 2 show the time achieved in this machine.
Figure 1. The Actual with a Predictive Technique
SETUP TIME
0
2
4
6
8
10
12
14
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
NUMBER OF SETUPS
TIME
Target (yellow)
Actual Time (blue)
Linear Trend Line (red)
Moving Average
(of Past 5 Setups) (green)
(blue)
(red)
(green)
(yellow)
(pink)

Figure 2. The Limits of Acceptance (Framework with hatches)
In Fig. 1, it can be depicted that the time reduces to a minimum, which cannot be reduced further. However, it is
noticed that the target time is achieved. The setup time trends downward within the upper and lower limits. Fig. 2 shows
the setup time behaviour of this machine as it reaches a firm setup time. This is as shown in the hatch lines. The gradient
of the hatch line indicates that if setup time is reduced to the lower tip, then set up time is achieved on an earlier target
than the upper tip. Ideally, the time to achieve is earlier for the upper tip. However, it is to be noted that this means
exhaustion of the workers. This is discussed in the later section.
The Predictive Equations (Moving Average and Exponential Smoothing)
The following are two predictive methods to calculate the estimated prediction time. These methods are outlined and
defined below.
Moving Average (MA) is the average of past facts. If there are two (2) observable times, then the average of the past
two will be the next prediction value.
TMA = (TN +… + T-2 + T-1 + T0) / (N +1) - [1] ; where P1 (the prediction value) is TMA
Exponential Smoothing (ES) is the weighted factor of an actual past value and a future prediction value. An initial
guess value of P0 is required.
TES = T0 + (1-) P0 - [2] ; where P1 (the prediction value) is TES
 Smoothing Constant
The prediction error is the difference between the actual and the predicted value. It is not possible to achieve a zero
error.
Moving average technique estimated prediction time calculated as follows. The last actual recorded time is 4.55 hours.
Therefore, the prediction estimate for this technique is 3.90 hours with a cumulative error limit of +/- 0.3 hours (approx.
20 minutes).
The exponential smoothing technique estimated prediction time calculated as follows. The last actual recorded time
is 4.55 hours. Therefore, the prediction estimate for this technique is 4.84 hours with a cumulative error limit of +/- 0.2
hours (approx. 10 minutes).
A COMPARISON OF METHODS – A REDUCTION TREND BASED ON MACHINE SETUP TIME
Probability of Occurrence on The Time Trend
The result is shown in the graph below comparing the above techniques with the probability of occurrence in a defined
range.
Target
Initial
Acceptable Limit
Lower
Upper
Lower
Tip
Upper
Tip

Figure 3. Most Probable Range
In the graph above, the most probable range is shown. The occurrences in each of the ranges are shown below.
Table 1. The Probable Range and Occurrences
Probable Range Occurrence
Between 6 – 8 hours 2
---------------------------------------------------------
Probable Range Occurrence
Between 6 – 8 hours 2 mode occurrences 1
Between 4 – 6 hours 2 mode occurrences 2
---------------------------------------------------------
Between 5 – 7 hours 1 (x4)
---------------------------------------------------------
Between 4.4 – 6.4 hours 1 see calculation below
Because most of the occurrences happened in the range 4 to 6 and 6 to 8 hours, with two occurrences in each range,
the result in the most probable range is between 4 to 8 hours. A calculation of the average time shows that the average is
between 4.4 to 6.4 hours. The calculation is as below:
Calculations:
Minimum Range: [(5hours x 4occurrences) + (2hours x 1 occurrence)] / 5occurrences
= (20 + 2) / 5
= 22 / 5
= 4.4 hours
Maximum Range: [(7hours x 4occurrences) + (4hours x 1 occurrence)] / 5occurrences
= (28 + 4) / 5
= 32 / 5
= 6.4 hours
The probability of a machine set up in this range is high.
SETUP TIME
0
2
4
6
8
10
12
14
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
NUMBER OF SETUPS
TIME
1 2
3 4
5
Most
probable
range

Categorisation of Mode Occurrence on The Time Trend
In categorising the type of trend at an instantaneous time, straight lines are drawn across the actual time record to
determine which category the setup activity is in. An average of equal division is applied here. This is shown in the graph
below.
Figure 4. Categories of Setup Time
In this case, it is shown here that the behaviour of the time trend is reducing to Category C with the possibility that it
will maintain within category C, as shown in Fig. 4. The mode occurrences in each of the categories are as follows:
Table 2. Occurrences in each of the categories
Category
Occurrence
A 1
B 9
C 16
D 0
Category A: 9 to 12 hours
Category B: 6 to 9 hours
Category C: 3 to 6 hours
Category D: Up to 3 hours
From the above Table 2, the mode occurrence based on the categories above is "Category C". The occurrence(s)
percentage in each of the categories above is 3.9% for Category A, 34.6% for Category B, 61.5% for Category C, and nil
for Category D. Category in access of fifty per cent shows a high number of occurrences.
RESULTS
Two arithmetic equations are utilised here to estimate a prediction. These two methods are straightforward with
equations to refer to. The prediction error is simply the summation of the actual minus the prediction value. The other
method of comparison is probability and categorical range. Probability is based on the number of occurrences in a defined
box (or range, here defined as 2 hours time frame in every five setups). In this case, the most probable upper limit appears
to be higher than the two-time trend technique mentioned here. The categorical range, here defined, is the mode
occurrence, where the mode occurrence shows that the upper limit is within the target setting.
All three methods (time trend technique, probability and categorisation) show that the time has reduced to a certainty.
Although there are instances where time increases after a reduction, the target time was met at the end of the project. This
SETUP TIME
0
2
4
6
8
10
12
14
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
NUMBER OF SETUPS
TIME
5
Category A
Category B
Category C
Category D

results in functional modes. Repetitive tasks become a functional routine within the limit. The methods above show a
consistent result of trend-setting. The overall achievement is accomplished.
Maximum and Minimum Time
The maximum time here is the maximum time taken to set up the machine. The minimum time is the minimum time
taken to set up the machine. Based on graph Trend 1 above, the maximum time is 8.45 hours, and the minimum is 3.25
hours.
The maximum or minimum time is important to know because if the setups are achieved in a short duration, the job
performer will be exhausted to perform subsequent setups. The quality of work is unimaginable because each time the
machine is required to be set up, the job performers performing the work will not want to do the work again, considering
the exhaustion involved. In addition, it will cause the job performer to raise the query of the unjustified change order.
With regards to this observable trend, it is noted that there is a reduction in time as shown. The job performers cooperated
with the operators and performed the tasks with considerable effort.
However, it is also noted that a maximum time is required to be known due to concerns about the unachievable
target. The maximum time not to be exceeded is not seen in the trend due to a reducing trend. In the situation, as shown
above, the reducing time trend shows the minimum achievable time not less than 3 hours. In case of sporadic occurrences,
the target line is above to detect it. In the case of predicting the sporadic occurrence, it remains a factor that is not able to
avoid, or an unknown, till it occurs. However, the three methods discussed confirm that the setup time is below the target
line, assuring that subsequent setups will be within the target and under control. A gradual increasing trend is not noticed
here. The target setting is discussed in the next section.
Target Setting
If the setup time is set too high, the machine will not be flexible. The number of machine setups will be less frequent.
If a target is set too low, the job performers will be exhausted. What is a realistic target? Now that it is known that the
trend is as shown, the target should be known.
It is known that two job performers will reduce the time by half. However, it is also required to know the path of the
task. If it is a critical task, then ideally, two men will reduce the time by half. If two persons on two separate occasions
can do two tasks, then two persons will reduce the time by half.
Studying the critical activities, the time can be reduced by a fraction of the total setup time. The time can be reduced
after it is implemented to a certain extent, based on the number of movements.
The blue line in trend 1 shows the time before a minor modification along with the critical activity. The pink line
shows the time after the change. For example, it is estimated that reducing four repetitive tasks to one in one of the
sections will reduce setup time to 5 minutes of work simply by moving up the section using a chain block and slotting in
a buckle pin.
The time recorded shows the trend before and after the modification. It is noticed that a target to reduce the time from
30 to 5 minutes resulted in the graph above. The actual time of an intended target setting of 25 minutes is recorded above.
A time reduction is justified by an estimated time based on the number of movements, which concluded to an estimated
reduction of 25 minutes of an actual approximate—the average time before the modification is 4.5 hours (approx. overall),
based on x number of setups. The average time after the modification is 4 hours (approx. overall), based on y number of
setups. A reduction of 1/2 hours.
The Possible Behaviors – Step Reduction or Steep Reduction
Figure 5. Step Improvements
Target
Initial
Acceptable Limit
Lower
Upper
Step Reduction

Step Reduction - The above is a step reduction curve. Short vertical lines show a small reduction. Long vertical lines
show huge step improvements. If there is an instance where an increment follows a step reduction, then it means that time
has increased. Past improvements have countered reduction. The effort is lost due to work strain or fitters unable to attend
to the activity. The step reduction above is charted within the upper and lower limits of a target time, representing the
reduction.
Figure 6. Steep Curves
Steep Reduction - The model above shows three possible types of reduction curves, C1, C2, and C3. All these curves
show a gradual reduction, with C3 being the steepest. When a steep reduction is noticed, the time to achieve the set target
is faster than a gradual reduction. C1 is the most gradual reduction curve in this case, whereas C3 is the steepest. All three
are within the upper or the lower limits. All three characteristics are below the set target within a time frame.
The actual time is shown in Fig. 1. There is an overall reduction in time. At each time interval, the setup time
sometimes reduces and sometimes increases. Overall, time reduces, and a reduction is observed. The curve shows a C2
type characteristic. Setup time is achieved below the target setting, within the upper and lower limits. The behaviour is
considered to be gradual.
Movements Estimate - The number of movements can be listed on a sheet with the time to perform the work recorded
besides the list of tasks. The critical path analysis method identifies the activity and hours of critical activities. By
recording past work time, a time can be estimated for a new task. In this case, moving up or down a vertical section using
a chain block and inserting a pin in a slot hole is estimated. The height to move up or down with precise accuracy can be
known. Slotting in the pin or removing it if not required is only a relief job. Movement estimate is calculating the number
of steps and estimating a time. So simple it is, the result is recorded and plotted in Fig. 1 above (blue and pink trend).
CONCLUSION
In conclusion, it is noticed that improvements along the machine are required to reduce the machine's setup. The
intended reduction was reduced and achieved during the duration of the project. Remarkably, these changes have impacted
the requirement to perform the calculated number of work tasks to estimate a reduction based on the number of
movements. The actual is seen in the graph above.
In order to predict an estimated time, it is required to set a reasonable target and achieve the intended purpose by
calculating the number of movements in a setup. As a result, as shown in the graphs above, the estimated time reduction
of 25 minutes is shown by the difference between the blue trend and the pink trend.
Utilising time trending techniques and equations to predict returns at an estimated time. The number of tasks
(movements estimate) will identify the items to reduce and to know the actual later. The estimated time as calculated is
compared to other techniques, probability, and categorisation. The results show consistency within a range.
Targe
t
Initial
Acceptable Limit
Lower
Upper
Steep Reduction
C1
C2
C3

ACKNOWLEDGEMENT
The author would like to thank his senior management for the vast opportunities given in the past to enable data and
analysis to be conducted in the manufacturing facilities.
REFERENCES
Seiichi Nakajima (1989). TPM Development Program: Implementing Total Productive Maintenance. Cambridge,
Massachusetts: Productivity Press
Robert H. Rosenfeld & David C. Wilson (1999). Managing Organisations: Text, Readings and Cases. London; New York:
McGraw-Hill
Barrie G. Dale (1990). Managing Quality. London; New York: Prentice Hall
Nigel Slack (1995) Operations Management. London, England: Pitman Publishing
George E.P. Box, Gwilym M. Jenkins, Gregory C. Reinsel & Greta M. Ljung (2015). Time Series Analysis: Forecasting
and Control. : Hoboken, New Jersey: John Wiley & Sons
CONFLICT OF INTEREST
The author(s), as noted, certify that they have NO affiliations with or involvement in any organisation or agency with
any financial interest (such as honoraria; educational grants; participation in speakers' bureaus; membership, jobs,
consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-
financial interest (such as personal or professional relationships, affiliations, expertise or beliefs) in the subject matter or
materials addressed in this manuscript.
AUTHORS' BIOGRAPHY
Author Professional Picture
Author's Full Name: Gan Chun Chet
Author's Email: chun_gan@hotmail.com
Author Professional Bio (not more than 150 words): I am a licensed engineer, registered with the Board of Engineers
Malaysia. I worked in the general industry as well oil and gas industry – more than 16 years. As a contributor in this value
economy, writing articles to magazines (e.g. Supply Chain Asia, Singapore), conduct talks in local institution (e.g.
Malaysian Institute of Management MIM, Institute of Engineers Malaysia IEM), and publishing papers on Journal
in NIOSH, Malaysia is unusual to me, with my experience in these few esteem areas. I obtained my degree in Mechanical
Engineering in the University of Manchester, UK (BEng, 1996). Thereafter I complete my Master in Manchester School
of Management, University of Manchester Institute of Science and Technology UMIST, UK (MSc, 1997). I have awards
from this University linked to Ford Motor Company in Design subject, also a price winner in Fluid Mechanics throughout
the undergraduate degree program, where my experiments are in aircraft industry.

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